{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/inference-of-covid-19-epidemiological","title":"Inference of COVID-19 epidemiological distributions from Brazilian hospital data","arxiv_id":"2007.10317","date":"2020-08-24","proceeding":null,"authors":[],"abstract":"Knowing COVID-19 epidemiological distributions, such as the time from patient\nadmission to death, is directly relevant to effective primary and secondary\ncare planning, and moreover, the mathematical modelling of the pandemic\ngenerally. We determine epidemiological distributions for patients hospitalised\nwith COVID-19 using a large dataset ($N=21{,}000-157{,}000$) from the Brazilian\nSistema de Informa\\c{c}\\~ao de Vigil\\^ancia Epidemiol\\'ogica da Gripe database.\nA joint Bayesian subnational model with partial pooling is used to\nsimultaneously describe the 26 states and one federal district of Brazil, and\nshows significant variation in the mean of the symptom-onset-to-death time,\nwith ranges between 11.2-17.8 days across the different states, and a mean of\n15.2 days for Brazil. We find strong evidence in favour of specific probability\ndensity function choices: for example, the gamma distribution gives the best\nfit for onset-to-death and the generalised log-normal for\nonset-to-hospital-admission. Our results show that epidemiological\ndistributions have considerable geographical variation, and provide the first\nestimates of these distributions in a low and middle-income setting. At the\nsubnational level, variation in COVID-19 outcome timings are found to be\ncorrelated with poverty, deprivation and segregation levels, and weaker\ncorrelation is observed for mean age, wealth and urbanicity.","url_abs":"http://arxiv.org/abs/2007.10317v2","url_pdf":"http://arxiv.org/pdf/2007.10317v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"inference-of-covid-19-epidemiological","repo_url":"https://github.com/mrc-ide/Brazil_COVID19_distributions","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}